Research Fellow (Machine Learning for Materials Design)

The School of Materials Science and has evolved into a hub of excellence in its niche areas of research. As part of NTU’s College of Engineering, MSE is now recognised worldwide as a premier research institution with top universities, multinational corporations and R&D institutions as its research collaborators and funding partners.

We are seeking a Postdoctoral Fellow to contribute to a project focused on using deep learning techniques to predict and develop flexible thermoelectric materials with enhanced performance, aiming to advance applications in wearable electronics and energy harvesting.

Key Responsibilities:

• Train Machine Learning Potentials: Develop and train machine learning potentials using DFT datasets, and apply them in molecular dynamics simulations to study and predict material behaviors.

• Technical Proficiency: Utilize Python, MATLAB, DFT software (preferably VASP), and molecular dynamics tools to perform advanced computational research, with a strong foundation in deep learning and neural networks.

• Collaborate and Communicate: Work closely with the research team, effectively communicate findings, and contribute to collaborative projects.

• Publish Research: Aim to publish high-impact research papers and present findings at top-tier conferences.

Job Requirements:

• PhD degree in Physics, Chemistry, Materials Science, or related areas, with a focus on computational modeling, machine learning for materials science, or thermoelectric materials.

• Experience in the development of machine learning potentials, preferably with interdisciplinary research experience in computational materials science.

• Strong written and verbal communication skills in English.

• Strong publication record in high-impact journals.

We regret that only shortlisted candidates will be notified.